Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must disclose behavior on its own. It does disclose the lookup outcome shape, including a failure mode ({found:False, query}), but it does not explicitly state whether the operation is read-only, what happens on ambiguous matches, or whether it requires special permissions. This is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: first the core purpose, then the usage context, then the return shape. Every sentence adds useful information, and there is no filler or repetition of schema details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simple single-parameter interface and the presence of an output schema, the description is nearly complete. It explains the disambiguation role, provides usage timing, and summarizes the return shape. It could be more complete by explicitly noting the operation is safe/read-only, but nothing critical is missing for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only defines 'query' as a string with no description, so the tool description carries the burden. It compensates by clarifying that the query is likely an informal or abbreviated AI tech/model name, such as compact model spellings, which adds meaning beyond the raw schema. A more explicit 'query is the name/alias to resolve' would make it fully unambiguous.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Disambiguate') and resource ('an AI tech or model name to a canonical entity'), making the tool's core function immediately clear. It also frames the tool as a prerequisite step before cost/trend/facts/workaround tools, which helps distinguish it from sibling tools like fetch_entity_facts or get_entity_trend.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to use it: before cost/trend/facts/workaround tools when the user's name may be informal or abbreviated. It gives a concrete example ('compact model spellings') but does not state when not to use it or name specific alternative tools, so it stops short of a full 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.